John teamed up with software developers at the University of Utah to create an algorithm called Pet Match. The program uses machine learning and computer vision to detect a dog's unique differentiating features, such as eye shape and fur color. Because of their fur, it's much harder to apply facial-recognition technology to canines than humans.

Once you submit a photo of your dog, their profile is added to the database and can be matched with pictures of lost pups. The better the photo, the more accurate the results. John claims that with a good picture, your dog will come up 95 percent of the time out of 100 dogs of the same breed.

The app has features to help capture searchable images, such as a bark noise to get dogs to look at the phone's camera and movable circles to focus the eye and nose data search points. It took me a few tries to get a decent picture!

If you find a lost dog, you can still take a picture at a distance and search for matches, it just won't be as precise. You can also view all missing and found pets in your area by list or map. In just a few weeks Finding Rover has already helped several pups find their way back home.

Finding Rover has great potential to help reunite pups with their families. The app makes it quick and easy to search for potential matches, hopefully encouraging more people to report stray pets. I can see this revolutionizing how animals are found, as long as enough people create profiles for the database to be valuable. Right now there doesn't seem to be much activity in my area.

Believe it or not, a version for cats is in the works and should be available in about six months!